Using in vivo EPID images to detect and quantify patient anatomy changes with gradient dose segmented analysis

To investigate the utility of gradient dose segmented analysis (GDSA) in combination with in vivo electronic portal imaging device (EPID) images to predict changes in the PTV mean dose for patient cases. Also, we use the GDSA to retrospectively analyze patients treated in our clinic to assess deviations for different treatment sites and use time-series data to observe any day-to-day changes.

In vivo EPID transit images acquired on the Varian Halcyon were analyzed for simulated errors in a phantom, including gas bubbles, weight loss, patient shifts, and an arm erroneously in the field. GDSA threshold parameters were tuned to maximize the coefficient of determination (R² ) between GDSA metrics and the change in the PTV mean dose (D_mean ) as estimated in a treatment planning system (TPS). Similarly for a gamma analysis, the gamma criteria were adjusted to maximize R² between gamma pass rate and the change in the PTV D_mean from the TPS. The predictive accuracy of these models was tested on patient data measuring the mean and standard deviation of the difference in the predicted change in PTV D_mean and the change in PTV D_mean measured in the TPS. This analysis was extended retrospectively for every patient treated over a 23-month period (n = 852 patients) to assess the range of expected deviations that occurred during routine clinical operation, as well as to assess any differences between treatment sites. Grouping patients treated on the same day, a time-series analysis was performed to determine if GDSA metrics could add value in tracking machine behavior over time.

For the phantom data, analyzing the errors, except for shifts, and comparing the change in PTV D_mean and GDSA mean, a maximal R²  = 0.90 was found for a dose threshold of 5% and gradient threshold of 3 mm. For the gamma approach a linear fit between the gamma pass rate for change in the PTV D_mean was assessed for different criteria, using the same image data. A maximal, R²  = 0.84 was found for a gamma criteria of 3%/3 mm, 45% lower dose threshold. For patient data, the predictive accuracy of the change in the PTV D_mean using the GDSA approach and the gamma approach was 0.09 ± 0.98 % and - 0.65 ± 2.21%, respectively. Comparing the two approaches the accuracy did not significantly differ (P = 0.38), whereas the precision of the GDSA prediction is significantly less (P < 0.001). The dosimetric impact of shifts was not detectable with either the GDSA or gamma approach. Analysis of all patients treated over 23 months showed that over 95% of fractions treated deviated from the first fraction by 2% or less. Deviations> 2% occurred most frequently for the later fractions of head-and-neck and lung treatments. Additionally, averaging the GDSA mean metric over all patients on a given treatment day showed that changes in the machine output on the order of 1% could be identified.

GDSA of in vivo EPID images is a useful technique for monitoring patient changes during the course of treatment, particularly weight loss and tumor shrinkage. The GDSA mean provides a quantitative estimate of the change in the PTV D_mean , giving a simple, quantitative metric by which to flag patients with clinically meaningful deviations in treatment. Averaging the GDSA metric over all patients treated on a given day and tracking daily variations can also provide a flag for any systematic deviations in treatment due to machine performance.